@inproceedings {39516,
	title = {GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation},
	journal = {26th International Conference on Pattern Recognition},
	year = {2022},
	publisher = { IEEE},
	abstract = {Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop when trained and tested on different distributions and therefore, require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations by performing multi-scale fusion operations across all resolution scales through dense connections while preserving low-level information. To further leverage scale information, we design cross multi-scale attention (CMSA) module that uses multi-scale features to identify, keep, and propagate informative features. Additionally, we introduce multi-scale feature selection (MSFS) modules to perform channel-wise attention that gates irrelevant features gathered through global multi-scale fusion within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of our network.\&nbsp;Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34\% and 10.31\% on unseen CVC-ClinicDB and on unseen Kvasir-SEG, in terms of dice coefficient. Additionally, when tested on unseen CVC-ColonDB, we surpass the state-of-the-art method by 9.38\% and 4.04\% in terms of dice coefficient, when source dataset is Kvasir-SEG and CVC-ClinicDB, respectively.},
	doi = {10.1109/ICPR56361.2022.9956726},
	author = {Srivastava, Abhishek and Chanda, Sukalpa and Jha, Debesh and Pal, Umapada and Ali, Sharib}
}
@article {27774,
	title = {A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging},
	journal = {Medical Image Analysis},
	volume = {70},
	year = {2021},
	pages = {102007},
	publisher = {Elsevier},
	abstract = {Gastrointestinal endoscopy has\&nbsp;been\&nbsp;an\&nbsp;active field\&nbsp;of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. \&nbsp; The high missed rate of such abnormalities during endoscopy is thus a\&nbsp; critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic\&nbsp; GI\&nbsp; disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the\&nbsp; Medico\&nbsp; GI\&nbsp; challenges:\&nbsp; Medical\&nbsp; Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018,\&nbsp; and\&nbsp; BioMedia\&nbsp; ACM\&nbsp; MM\&nbsp; Grand\&nbsp; Challenge\&nbsp; 2019.\&nbsp; These challenges are initiative to set-up\&nbsp; a\&nbsp; benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a\&nbsp; period of three consecutive years and provide a\&nbsp; detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings.\&nbsp; Our analysis revealed that the participants achieved an improvement on maximum\&nbsp; Mathew correlation coefficient\&nbsp; (MCC)\&nbsp; from\&nbsp; 82.68\%\&nbsp; in\&nbsp; 2017\&nbsp; to\&nbsp; 93.98\%\&nbsp; in\&nbsp; 2018\&nbsp; and 95.20\% in 2019 challenges, and a significant increase in computational speed over consecutive years.},
	keywords = {Artificial intelligence, BioMedia 2019 Grand Challenge, Computer-aided detection and diagnosis, Gastrointestinal endoscopy challenges, Medical imaging, Medico Task 2017, Medico Task 2018},
	doi = {https://doi.org/10.1016/j.media.2021.102007},
	author = {Jha, Debesh and Ali, Sharib and Hicks, Steven and Thambawita, Vajira and Borgli, Hanna and Pia H. {Smedsrud} and de Lange, Thomas and Pogorelov, Konstantin and Wang, Xiaowei and Harzig, Philipp and Tran, Minh-Triet and Meng, Wenhua and Hoang, Trung-Hieu and Dias, Danielle and Tobey H. {Ko} and Agrawal, Taruna and Ostroukhova, Olga and Khan, Zeshan and Muhammad Atif {Tahir} and Liu, Yang and Chang, Yuan and Kirker{\o}d, Mathias and Johansen, Dag and Lux, Mathias and H{\r a}vard D. {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27635,
	title = {DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation},
	journal = {25th International Conference on Pattern Recognition},
	year = {2021},
	pages = {307-314},
	publisher = { Springer},
	abstract = {Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions), an automated, accurate, and fast segmentation can improve the quality of delineated lesion boundaries and reduce missed rate. The Endotect challenge provides an opportunity to benchmark computer vision methods by training on the publicly available Hyperkvasir and testing on a separate unseen dataset. In this paper, we propose a novel architecture called {\textquoteleft}{\textquoteleft}DDANet{\textquoteright}{\textquoteright} based on a dual decoder attention network. Our experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577, demonstrating the generalization ability of our model.},
	keywords = {Benchmarking, Convolutional neural network, deep learning, Polyp segmentation},
	author = {Nikhil Kumar {Tomar} and Jha, Debesh and Ali, Sharib and H{\r a}vard D. {Johansen} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {28020,
	title = {Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy},
	journal = {2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)},
	year = {2021},
	pages = {1-4},
	publisher = {IEEE},
	abstract = {Minimally Invasive Surgery (MIS) is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have open potential for computer-assisted procedures. However, there exists challenges and requirement to improve detection and tracking of the position of the instruments during these surgical procedures. To this end, we evaluate and compare some popular deep learning methods that can potentially be explored for the automated segmentation of surgical instruments in laparoscopy, an important step towards tool tracking. Our experimental results demonstrate that the Dual decoder attention network (DDANet) produces a superior result compared to other recent deep learning methods. DDANet produces a dice coefficient of 0.8739 and mean intersection over union of 0.8183 for the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019 dataset, at a real-time speed of 101.36 frames per second which is critical for such procedures.},
	keywords = {Real-time segmentation, minimally invasive surgery, surgical instruments,  laparoscopy, deep learning},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and Riegler, Michael and Johansen, Dag and H{\r a}vard D {Johansen} and Halvorsen, P{\r a}l}
}
@inproceedings {27857,
	title = {Improving generalizibilty in polyp segmentation using ensemble convolutional neural network},
	journal = {3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV2021)},
	volume = {2886},
	year = {2021},
	publisher = {CEUR Workshop Proceedings},
	abstract = {Medical image segmentation is a crucial task in medical image analysis. Despite near expert-label performance with the application of the deep learning method in medical image segmentation, the generalization of such models in the clinical environment remains a significant challenge. Transfer learning from a large medical dataset from the same domain is a common technique to address generalizability. However, it is difficult to find a similar large medical dataset. To address generalizability in polyp segmentation, we have used an ensemble of four MultiResUNet architectures, each trained on the combination of the different centered datasets provided by the challenge organizers. Our method achieved a decent performance of 0.6172 {\textpm} 0.0778 for the multi-centered dataset. Our study shows that significant work needs to be done to develop a computer-aided diagnosis system to detect and localize polyp of the multi-center datasets, which is essential for improving the quality of the colonoscopy.},
	keywords = {colonoscopy, Convolutional neural network, health informatics, Polyp segmentation},
	author = {KumarTomar, Nikhil and Ibtehaz, Nabil and Jha, Debesh and Halvorsen, P{\r a}l and Ali, Sharib}
}
@inproceedings {27511,
	title = {Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy},
	journal = {27th International Conference on Multimedia Modeling},
	volume = {LNCS, volume 12573},
	year = {2021},
	pages = {218-229},
	publisher = {Springer},
	abstract = {Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the\&nbsp; {\textquoteleft}{\textquoteleft}Kvasir Instrument{\textquoteright}{\textquoteright} dataset which consists of $590$ annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple instruments, while the best result for both methods was observed on all other types of images. Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements.\&nbsp; Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.\&nbsp;},
	author = {Jha, Debesh and Ali, Sharib and Emanuelsen, Krister and Hicks, Steven and Thambawita, Vajira and Garcia-Ceja, Enrique and Riegler, Michael and de Lange, Thomas and Peter T. {Schmidt} and H{\r a}vard D. {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {27856,
	title = {NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy},
	journal = {34th IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2021},
	publisher = { IEEE},
	abstract = {Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting model size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.},
	keywords = {colonoscopy, deep learning, segmentation, tool  segmentation, Video capsule endoscopy},
	author = {Jha, Debesh and Nikhil Kumar {Tomar} and Ali, Sharib and Riegler, Michael and H{\r a}vard D {Johansen} and Johansen, Dag and de Lange, Thomas and Halvorsen, P{\r a}l}
}
@article {27773,
	title = {Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	pages = {40496-40510},
	publisher = { IEEE},
	abstract = {Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localization, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localization task. Likewise, the proposed ColonSegNetachieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimize miss-detection rates.},
	keywords = {Medical image segmentation,  ColonSegNet, colonoscopy, polyps, deep learning, detection, localization, benchmarking, Kvasir-SEG},
	doi = {10.1109/ACCESS.2021.3063716},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and H{\r a}vard D {Johansen} and Johansen, Dag and Rittscher, Jens and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {39534,
	title = {Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy},
	journal = {International Conference on Multimedia Modeling},
	year = {2020},
	publisher = { Springer},
	abstract = {Gastrointestinal\&nbsp;pathologies are periodically screened, biopsied, and resected using surgical tools. Usually, the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development,\&nbsp; amount, and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic, we have released the\&nbsp; {\textquoteleft}{\textquoteleft}Kvasir-Instrument{\textquoteright}{\textquoteright} dataset, which consists of 590\&nbsp;annotated frames containing GI\&nbsp;procedure tools such as snares, balloons, and biopsy forceps, etc. Besides the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI\&nbsp;endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158\&nbsp;and a Jaccard index of 0.8578\&nbsp;using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple tools, while the best result for both methods was observed on all other types of images. Both qualitative and quantitative results show that the model performs reasonably good, but there is potential for further improvements.\&nbsp; Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI\&nbsp;endoscopy.},
	keywords = {Benchmarking, Convolutional  neural network, Gastrointestinal endoscopy, Tool segmentation},
	url = {https://www.springerprofessional.de/en/kvasir-instrument-diagnostic-and-therapeutic-tool-segmentation-d/18788368},
	author = {Jha, Debesh and Ali, Sharib and Emanuelsen, Krister and Hicks, Steven and Thambawita, Vajira and Garcia-Ceja, Enrique and Riegler, Michael and de Lange, Thomas and Peter T. {Schmidt} and H{\r a}vard D {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l}
}
